Overview

Dataset statistics

Number of variables24
Number of observations1287
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory241.4 KiB
Average record size in memory192.1 B

Variable types

Numeric12
Categorical12

Alerts

imdb_id has a high cardinality: 1287 distinct valuesHigh cardinality
original_title has a high cardinality: 1280 distinct valuesHigh cardinality
cast has a high cardinality: 1278 distinct valuesHigh cardinality
homepage has a high cardinality: 1266 distinct valuesHigh cardinality
director has a high cardinality: 789 distinct valuesHigh cardinality
tagline has a high cardinality: 1283 distinct valuesHigh cardinality
keywords has a high cardinality: 1272 distinct valuesHigh cardinality
overview has a high cardinality: 1287 distinct valuesHigh cardinality
genres has a high cardinality: 496 distinct valuesHigh cardinality
production_companies has a high cardinality: 1138 distinct valuesHigh cardinality
release_date has a high cardinality: 1080 distinct valuesHigh cardinality
Unnamed: 0 is highly overall correlated with id and 1 other fieldsHigh correlation
id is highly overall correlated with Unnamed: 0 and 1 other fieldsHigh correlation
popularity is highly overall correlated with budget and 5 other fieldsHigh correlation
budget is highly overall correlated with popularity and 4 other fieldsHigh correlation
revenue is highly overall correlated with popularity and 5 other fieldsHigh correlation
vote_count is highly overall correlated with popularity and 5 other fieldsHigh correlation
release_year is highly overall correlated with Unnamed: 0 and 1 other fieldsHigh correlation
budget_adj is highly overall correlated with popularity and 5 other fieldsHigh correlation
revenue_adj is highly overall correlated with popularity and 5 other fieldsHigh correlation
profit is highly overall correlated with popularity and 4 other fieldsHigh correlation
imdb_id is uniformly distributedUniform
original_title is uniformly distributedUniform
cast is uniformly distributedUniform
homepage is uniformly distributedUniform
tagline is uniformly distributedUniform
keywords is uniformly distributedUniform
overview is uniformly distributedUniform
production_companies is uniformly distributedUniform
release_date is uniformly distributedUniform
popularity_level is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
id has unique valuesUnique
imdb_id has unique valuesUnique
overview has unique valuesUnique
revenue_adj has unique valuesUnique

Reproduction

Analysis started2023-01-31 16:17:18.843366
Analysis finished2023-01-31 16:17:36.597991
Duration17.75 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1287
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4125.843
Minimum0
Maximum10760
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-01-31T08:17:36.683460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile94.6
Q11972
median3523
Q36554.5
95-th percentile8706.1
Maximum10760
Range10760
Interquartile range (IQR)4582.5

Descriptive statistics

Standard deviation2671.9366
Coefficient of variation (CV)0.64760984
Kurtosis-0.8209793
Mean4125.843
Median Absolute Deviation (MAD)1993
Skewness0.3638638
Sum5309960
Variance7139245.1
MonotonicityStrictly increasing
2023-01-31T08:17:36.768655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.1%
4715 1
 
0.1%
5449 1
 
0.1%
5447 1
 
0.1%
5445 1
 
0.1%
5442 1
 
0.1%
5438 1
 
0.1%
5437 1
 
0.1%
5434 1
 
0.1%
5433 1
 
0.1%
Other values (1277) 1277
99.2%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
ValueCountFrequency (%)
10760 1
0.1%
10759 1
0.1%
10724 1
0.1%
10689 1
0.1%
10595 1
0.1%
10594 1
0.1%
10489 1
0.1%
10438 1
0.1%
10401 1
0.1%
10338 1
0.1%

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1287
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52557.491
Minimum11
Maximum333348
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-01-31T08:17:36.883125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile348.6
Q15851.5
median20178
Q362209.5
95-th percentile249483
Maximum333348
Range333337
Interquartile range (IQR)56358

Descriptive statistics

Standard deviation74450.077
Coefficient of variation (CV)1.4165455
Kurtosis3.3461861
Mean52557.491
Median Absolute Deviation (MAD)19466
Skewness2.0238079
Sum67641491
Variance5.542814 × 109
MonotonicityNot monotonic
2023-01-31T08:17:36.985102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135397 1
 
0.1%
71679 1
 
0.1%
136400 1
 
0.1%
80274 1
 
0.1%
72190 1
 
0.1%
47964 1
 
0.1%
138843 1
 
0.1%
87421 1
 
0.1%
93456 1
 
0.1%
152601 1
 
0.1%
Other values (1277) 1277
99.2%
ValueCountFrequency (%)
11 1
0.1%
12 1
0.1%
14 1
0.1%
22 1
0.1%
24 1
0.1%
28 1
0.1%
35 1
0.1%
38 1
0.1%
58 1
0.1%
65 1
0.1%
ValueCountFrequency (%)
333348 1
0.1%
328589 1
0.1%
328425 1
0.1%
325348 1
0.1%
323272 1
0.1%
321741 1
0.1%
320588 1
0.1%
318846 1
0.1%
314365 1
0.1%
312221 1
0.1%

imdb_id
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct1287
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
tt0369610
 
1
tt1855325
 
1
tt1272878
 
1
tt1731141
 
1
tt0816711
 
1
Other values (1282)
1282 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters11583
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1287 ?
Unique (%)100.0%

Sample

1st rowtt0369610
2nd rowtt1392190
3rd rowtt2908446
4th rowtt2488496
5th rowtt2820852

Common Values

ValueCountFrequency (%)
tt0369610 1
 
0.1%
tt1855325 1
 
0.1%
tt1272878 1
 
0.1%
tt1731141 1
 
0.1%
tt0816711 1
 
0.1%
tt1606378 1
 
0.1%
tt1457767 1
 
0.1%
tt1411250 1
 
0.1%
tt1690953 1
 
0.1%
tt1798709 1
 
0.1%
Other values (1277) 1277
99.2%

Length

2023-01-31T08:17:37.067850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tt0369610 1
 
0.1%
tt1013743 1
 
0.1%
tt2908446 1
 
0.1%
tt2488496 1
 
0.1%
tt2820852 1
 
0.1%
tt1663202 1
 
0.1%
tt1340138 1
 
0.1%
tt3659388 1
 
0.1%
tt2293640 1
 
0.1%
tt1964418 1
 
0.1%
Other values (1277) 1277
99.2%

Most occurring characters

ValueCountFrequency (%)
t 2574
22.2%
0 1475
12.7%
1 1316
11.4%
2 904
 
7.8%
4 883
 
7.6%
3 830
 
7.2%
8 776
 
6.7%
7 727
 
6.3%
6 710
 
6.1%
9 703
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9009
77.8%
Lowercase Letter 2574
 
22.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1475
16.4%
1 1316
14.6%
2 904
10.0%
4 883
9.8%
3 830
9.2%
8 776
8.6%
7 727
8.1%
6 710
7.9%
9 703
7.8%
5 685
7.6%
Lowercase Letter
ValueCountFrequency (%)
t 2574
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9009
77.8%
Latin 2574
 
22.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1475
16.4%
1 1316
14.6%
2 904
10.0%
4 883
9.8%
3 830
9.2%
8 776
8.6%
7 727
8.1%
6 710
7.9%
9 703
7.8%
5 685
7.6%
Latin
ValueCountFrequency (%)
t 2574
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11583
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 2574
22.2%
0 1475
12.7%
1 1316
11.4%
2 904
 
7.8%
4 883
 
7.6%
3 830
 
7.2%
8 776
 
6.7%
7 727
 
6.3%
6 710
 
6.1%
9 703
 
6.1%

popularity
Real number (ℝ)

Distinct1286
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7860221
Minimum0.010335
Maximum32.985763
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-01-31T08:17:37.158675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.010335
5-th percentile0.2859329
Q10.6647835
median1.152354
Q32.1253415
95-th percentile5.4437444
Maximum32.985763
Range32.975428
Interquartile range (IQR)1.460558

Descriptive statistics

Standard deviation2.172137
Coefficient of variation (CV)1.2161871
Kurtosis63.240427
Mean1.7860221
Median Absolute Deviation (MAD)0.608696
Skewness6.0176957
Sum2298.6104
Variance4.7181792
MonotonicityNot monotonic
2023-01-31T08:17:37.259035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.107689 2
 
0.2%
32.985763 1
 
0.1%
2.032167 1
 
0.1%
2.196946 1
 
0.1%
2.476989 1
 
0.1%
2.604638 1
 
0.1%
2.815499 1
 
0.1%
3.472358 1
 
0.1%
3.518275 1
 
0.1%
3.928789 1
 
0.1%
Other values (1276) 1276
99.1%
ValueCountFrequency (%)
0.010335 1
0.1%
0.015997 1
0.1%
0.021371 1
0.1%
0.028456 1
0.1%
0.040858 1
0.1%
0.050524 1
0.1%
0.06324 1
0.1%
0.075624 1
0.1%
0.076109 1
0.1%
0.086287 1
0.1%
ValueCountFrequency (%)
32.985763 1
0.1%
28.419936 1
0.1%
24.949134 1
0.1%
14.311205 1
0.1%
13.112507 1
0.1%
12.971027 1
0.1%
12.037933 1
0.1%
11.422751 1
0.1%
11.173104 1
0.1%
10.739009 1
0.1%

budget
Real number (ℝ)

Distinct228
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52003492
Minimum1
Maximum4.25 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-01-31T08:17:37.358969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2000000
Q114000000
median32000000
Q370000000
95-th percentile1.7 × 108
Maximum4.25 × 108
Range4.25 × 108
Interquartile range (IQR)56000000

Descriptive statistics

Standard deviation55145404
Coefficient of variation (CV)1.0604173
Kurtosis4.3678954
Mean52003492
Median Absolute Deviation (MAD)23500000
Skewness1.8587748
Sum6.6928495 × 1010
Variance3.0410156 × 1015
MonotonicityNot monotonic
2023-01-31T08:17:37.458854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000000 53
 
4.1%
40000000 51
 
4.0%
15000000 50
 
3.9%
20000000 45
 
3.5%
25000000 40
 
3.1%
50000000 36
 
2.8%
60000000 35
 
2.7%
35000000 35
 
2.7%
150000000 33
 
2.6%
10000000 29
 
2.3%
Other values (218) 880
68.4%
ValueCountFrequency (%)
1 1
0.1%
3 1
0.1%
30 1
0.1%
68 1
0.1%
75 1
0.1%
93 1
0.1%
7000 1
0.1%
8000 1
0.1%
15000 1
0.1%
17000 1
0.1%
ValueCountFrequency (%)
425000000 1
 
0.1%
380000000 1
 
0.1%
300000000 1
 
0.1%
280000000 1
 
0.1%
260000000 2
 
0.2%
258000000 1
 
0.1%
255000000 1
 
0.1%
250000000 7
0.5%
245000000 1
 
0.1%
237000000 1
 
0.1%

revenue
Real number (ℝ)

Distinct1285
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7624444 × 108
Minimum43
Maximum2.7815058 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-01-31T08:17:37.566733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile716278.1
Q125650970
median82087155
Q32.1406942 × 108
95-th percentile7.0979216 × 108
Maximum2.7815058 × 109
Range2.7815058 × 109
Interquartile range (IQR)1.8841845 × 108

Descriptive statistics

Standard deviation2.5381558 × 108
Coefficient of variation (CV)1.4401338
Kurtosis16.155231
Mean1.7624444 × 108
Median Absolute Deviation (MAD)70976180
Skewness3.1750379
Sum2.2682659 × 1011
Variance6.4422347 × 1016
MonotonicityNot monotonic
2023-01-31T08:17:37.883212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70000000 2
 
0.2%
2500000 2
 
0.2%
1513528810 1
 
0.1%
970761885 1
 
0.1%
131940411 1
 
0.1%
125537191 1
 
0.1%
531865000 1
 
0.1%
304654182 1
 
0.1%
318000141 1
 
0.1%
98337295 1
 
0.1%
Other values (1275) 1275
99.1%
ValueCountFrequency (%)
43 1
0.1%
46 1
0.1%
134 1
0.1%
193 1
0.1%
200 1
0.1%
1378 1
0.1%
7306 1
0.1%
15071 1
0.1%
18097 1
0.1%
30471 1
0.1%
ValueCountFrequency (%)
2781505847 1
0.1%
2068178225 1
0.1%
1845034188 1
0.1%
1519557910 1
0.1%
1513528810 1
0.1%
1506249360 1
0.1%
1405035767 1
0.1%
1327817822 1
0.1%
1274219009 1
0.1%
1215439994 1
0.1%

original_title
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1280
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
Halloween
 
2
The Three Musketeers
 
2
Wanted
 
2
The Thing
 
2
Halloween II
 
2
Other values (1275)
1277 

Length

Max length83
Median length45
Mean length15.009324
Min length2

Characters and Unicode

Total characters19317
Distinct characters95
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1273 ?
Unique (%)98.9%

Sample

1st rowJurassic World
2nd rowMad Max: Fury Road
3rd rowInsurgent
4th rowStar Wars: The Force Awakens
5th rowFurious 7

Common Values

ValueCountFrequency (%)
Halloween 2
 
0.2%
The Three Musketeers 2
 
0.2%
Wanted 2
 
0.2%
The Thing 2
 
0.2%
Halloween II 2
 
0.2%
Clash of the Titans 2
 
0.2%
The Fog 2
 
0.2%
Jurassic World 1
 
0.1%
The Conjuring 1
 
0.1%
Riddick 1
 
0.1%
Other values (1270) 1270
98.7%

Length

2023-01-31T08:17:38.006422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 419
 
12.0%
of 121
 
3.5%
a 43
 
1.2%
and 42
 
1.2%
in 35
 
1.0%
33
 
0.9%
2 27
 
0.8%
to 21
 
0.6%
man 18
 
0.5%
love 13
 
0.4%
Other values (1719) 2726
77.9%

Most occurring characters

ValueCountFrequency (%)
2211
 
11.4%
e 1968
 
10.2%
a 1192
 
6.2%
o 1172
 
6.1%
n 1071
 
5.5%
r 1051
 
5.4%
t 974
 
5.0%
i 968
 
5.0%
s 753
 
3.9%
h 731
 
3.8%
Other values (85) 7226
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13580
70.3%
Uppercase Letter 3092
 
16.0%
Space Separator 2212
 
11.5%
Other Punctuation 250
 
1.3%
Decimal Number 129
 
0.7%
Dash Punctuation 33
 
0.2%
Modifier Symbol 5
 
< 0.1%
Other Symbol 4
 
< 0.1%
Other Number 3
 
< 0.1%
Final Punctuation 2
 
< 0.1%
Other values (5) 7
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 448
14.5%
S 256
 
8.3%
M 199
 
6.4%
A 177
 
5.7%
B 177
 
5.7%
D 171
 
5.5%
P 151
 
4.9%
H 145
 
4.7%
C 145
 
4.7%
I 137
 
4.4%
Other values (21) 1086
35.1%
Lowercase Letter
ValueCountFrequency (%)
e 1968
14.5%
a 1192
 
8.8%
o 1172
 
8.6%
n 1071
 
7.9%
r 1051
 
7.7%
t 974
 
7.2%
i 968
 
7.1%
s 753
 
5.5%
h 731
 
5.4%
l 612
 
4.5%
Other values (16) 3088
22.7%
Other Punctuation
ValueCountFrequency (%)
: 105
42.0%
' 48
19.2%
. 40
 
16.0%
& 22
 
8.8%
, 20
 
8.0%
! 9
 
3.6%
? 2
 
0.8%
/ 2
 
0.8%
¡ 1
 
0.4%
· 1
 
0.4%
Decimal Number
ValueCountFrequency (%)
2 37
28.7%
3 26
20.2%
1 21
16.3%
0 18
14.0%
7 7
 
5.4%
5 6
 
4.7%
4 5
 
3.9%
8 5
 
3.9%
9 2
 
1.6%
6 2
 
1.6%
Modifier Symbol
ValueCountFrequency (%)
¸ 2
40.0%
´ 2
40.0%
¨ 1
20.0%
Other Number
ValueCountFrequency (%)
¼ 1
33.3%
½ 1
33.3%
³ 1
33.3%
Space Separator
ValueCountFrequency (%)
2211
> 99.9%
  1
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
° 3
75.0%
© 1
 
25.0%
Open Punctuation
ValueCountFrequency (%)
( 1
50.0%
1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%
Final Punctuation
ValueCountFrequency (%)
» 2
100.0%
Currency Symbol
ValueCountFrequency (%)
2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Other Letter
ValueCountFrequency (%)
ª 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16673
86.3%
Common 2644
 
13.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1968
 
11.8%
a 1192
 
7.1%
o 1172
 
7.0%
n 1071
 
6.4%
r 1051
 
6.3%
t 974
 
5.8%
i 968
 
5.8%
s 753
 
4.5%
h 731
 
4.4%
l 612
 
3.7%
Other values (48) 6181
37.1%
Common
ValueCountFrequency (%)
2211
83.6%
: 105
 
4.0%
' 48
 
1.8%
. 40
 
1.5%
2 37
 
1.4%
- 33
 
1.2%
3 26
 
1.0%
& 22
 
0.8%
1 21
 
0.8%
, 20
 
0.8%
Other values (27) 81
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19272
99.8%
None 42
 
0.2%
Currency Symbols 2
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2211
 
11.5%
e 1968
 
10.2%
a 1192
 
6.2%
o 1172
 
6.1%
n 1071
 
5.6%
r 1051
 
5.5%
t 974
 
5.1%
i 968
 
5.0%
s 753
 
3.9%
h 731
 
3.8%
Other values (65) 7181
37.3%
None
ValueCountFrequency (%)
Ð 14
33.3%
à 4
 
9.5%
Ñ 4
 
9.5%
° 3
 
7.1%
¸ 2
 
4.8%
» 2
 
4.8%
´ 2
 
4.8%
¼ 1
 
2.4%
Š 1
 
2.4%
 1
 
2.4%
Other values (8) 8
19.0%
Currency Symbols
ValueCountFrequency (%)
2
100.0%
Punctuation
ValueCountFrequency (%)
1
100.0%

cast
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1278
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
Elijah Wood|Ian McKellen|Viggo Mortensen|Liv Tyler|Orlando Bloom
 
3
Jennifer Lawrence|Josh Hutcherson|Liam Hemsworth|Woody Harrelson|Elizabeth Banks
 
3
Mike Myers|Eddie Murphy|Cameron Diaz|Julie Andrews|Antonio Banderas
 
2
Mark Hamill|Harrison Ford|Carrie Fisher|Billy Dee Williams|Anthony Daniels
 
2
Martin Freeman|Ian McKellen|Richard Armitage|Ken Stott|Graham McTavish
 
2
Other values (1273)
1275 

Length

Max length98
Median length85
Mean length69.337218
Min length9

Characters and Unicode

Total characters89237
Distinct characters95
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1271 ?
Unique (%)98.8%

Sample

1st rowChris Pratt|Bryce Dallas Howard|Irrfan Khan|Vincent D'Onofrio|Nick Robinson
2nd rowTom Hardy|Charlize Theron|Hugh Keays-Byrne|Nicholas Hoult|Josh Helman
3rd rowShailene Woodley|Theo James|Kate Winslet|Ansel Elgort|Miles Teller
4th rowHarrison Ford|Mark Hamill|Carrie Fisher|Adam Driver|Daisy Ridley
5th rowVin Diesel|Paul Walker|Jason Statham|Michelle Rodriguez|Dwayne Johnson

Common Values

ValueCountFrequency (%)
Elijah Wood|Ian McKellen|Viggo Mortensen|Liv Tyler|Orlando Bloom 3
 
0.2%
Jennifer Lawrence|Josh Hutcherson|Liam Hemsworth|Woody Harrelson|Elizabeth Banks 3
 
0.2%
Mike Myers|Eddie Murphy|Cameron Diaz|Julie Andrews|Antonio Banderas 2
 
0.2%
Mark Hamill|Harrison Ford|Carrie Fisher|Billy Dee Williams|Anthony Daniels 2
 
0.2%
Martin Freeman|Ian McKellen|Richard Armitage|Ken Stott|Graham McTavish 2
 
0.2%
Ewan McGregor|Natalie Portman|Hayden Christensen|Ian McDiarmid|Samuel L. Jackson 2
 
0.2%
Kristen Stewart|Robert Pattinson|Taylor Lautner|Ashley Greene|Peter Facinelli 2
 
0.2%
Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vincent D'Onofrio|Nick Robinson 1
 
0.1%
Vin Diesel|Karl Urban|Katee Sackhoff|Jordi Mollà|Bokeem Woodbine 1
 
0.1%
Denzel Washington|Mark Wahlberg|Paula Patton|Bill Paxton|Fred Ward 1
 
0.1%
Other values (1268) 1268
98.5%

Length

2023-01-31T08:17:38.132998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tom 33
 
0.4%
michael 22
 
0.3%
ben 22
 
0.3%
james 21
 
0.3%
jason 20
 
0.2%
de 20
 
0.2%
mark 20
 
0.2%
patrick 18
 
0.2%
l 18
 
0.2%
chris 17
 
0.2%
Other values (6385) 7893
97.4%

Most occurring characters

ValueCountFrequency (%)
e 7966
 
8.9%
a 7445
 
8.3%
6817
 
7.6%
n 6237
 
7.0%
| 5131
 
5.7%
i 5114
 
5.7%
r 5113
 
5.7%
o 4662
 
5.2%
l 4390
 
4.9%
s 3180
 
3.6%
Other values (85) 33182
37.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 63187
70.8%
Uppercase Letter 13663
 
15.3%
Space Separator 6820
 
7.6%
Math Symbol 5137
 
5.8%
Other Punctuation 243
 
0.3%
Dash Punctuation 84
 
0.1%
Other Symbol 45
 
0.1%
Format 14
 
< 0.1%
Modifier Symbol 13
 
< 0.1%
Initial Punctuation 11
 
< 0.1%
Other values (5) 20
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
J 1238
 
9.1%
M 1117
 
8.2%
C 1069
 
7.8%
S 1046
 
7.7%
B 1031
 
7.5%
R 807
 
5.9%
D 787
 
5.8%
A 757
 
5.5%
H 621
 
4.5%
K 606
 
4.4%
Other values (22) 4584
33.6%
Lowercase Letter
ValueCountFrequency (%)
e 7966
12.6%
a 7445
11.8%
n 6237
9.9%
i 5114
 
8.1%
r 5113
 
8.1%
o 4662
 
7.4%
l 4390
 
6.9%
s 3180
 
5.0%
t 3001
 
4.7%
h 2436
 
3.9%
Other values (19) 13643
21.6%
Other Punctuation
ValueCountFrequency (%)
. 153
63.0%
' 54
 
22.2%
¡ 17
 
7.0%
7
 
2.9%
, 5
 
2.1%
3
 
1.2%
2
 
0.8%
1
 
0.4%
§ 1
 
0.4%
Modifier Symbol
ValueCountFrequency (%)
¸ 10
76.9%
¯ 1
 
7.7%
¨ 1
 
7.7%
´ 1
 
7.7%
Other Number
ValueCountFrequency (%)
¼ 3
50.0%
³ 1
 
16.7%
² 1
 
16.7%
¹ 1
 
16.7%
Space Separator
ValueCountFrequency (%)
6817
> 99.9%
  3
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
| 5131
99.9%
± 6
 
0.1%
Other Symbol
ValueCountFrequency (%)
© 44
97.8%
1
 
2.2%
Initial Punctuation
ValueCountFrequency (%)
« 9
81.8%
2
 
18.2%
Currency Symbol
ValueCountFrequency (%)
¥ 7
77.8%
£ 2
 
22.2%
Other Letter
ValueCountFrequency (%)
ª 1
50.0%
º 1
50.0%
Decimal Number
ValueCountFrequency (%)
0 1
50.0%
5 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 84
100.0%
Format
ValueCountFrequency (%)
­ 14
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 76851
86.1%
Common 12386
 
13.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7966
 
10.4%
a 7445
 
9.7%
n 6237
 
8.1%
i 5114
 
6.7%
r 5113
 
6.7%
o 4662
 
6.1%
l 4390
 
5.7%
s 3180
 
4.1%
t 3001
 
3.9%
h 2436
 
3.2%
Other values (52) 27307
35.5%
Common
ValueCountFrequency (%)
6817
55.0%
| 5131
41.4%
. 153
 
1.2%
- 84
 
0.7%
' 54
 
0.4%
© 44
 
0.4%
¡ 17
 
0.1%
­ 14
 
0.1%
¸ 10
 
0.1%
« 9
 
0.1%
Other values (23) 53
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88954
99.7%
None 268
 
0.3%
Punctuation 14
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7966
 
9.0%
a 7445
 
8.4%
6817
 
7.7%
n 6237
 
7.0%
| 5131
 
5.8%
i 5114
 
5.7%
r 5113
 
5.7%
o 4662
 
5.2%
l 4390
 
4.9%
s 3180
 
3.6%
Other values (50) 32899
37.0%
None
ValueCountFrequency (%)
à 114
42.5%
© 44
 
16.4%
¡ 17
 
6.3%
­ 14
 
5.2%
à 11
 
4.1%
¸ 10
 
3.7%
« 9
 
3.4%
Ä 8
 
3.0%
¥ 7
 
2.6%
± 6
 
2.2%
Other values (19) 28
 
10.4%
Punctuation
ValueCountFrequency (%)
7
50.0%
3
21.4%
2
 
14.3%
1
 
7.1%
1
 
7.1%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%

homepage
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1266
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
http://www.thehungergames.movie/
 
4
http://www.missionimpossible.com/
 
4
http://www.thehobbit.com/
 
3
http://www.transformersmovie.com/
 
3
http://disney.go.com/disneypictures/pirates/
 
2
Other values (1261)
1271 

Length

Max length138
Median length77
Mean length36.724165
Min length18

Characters and Unicode

Total characters47264
Distinct characters73
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1251 ?
Unique (%)97.2%

Sample

1st rowhttp://www.jurassicworld.com/
2nd rowhttp://www.madmaxmovie.com/
3rd rowhttp://www.thedivergentseries.movie/#insurgent
4th rowhttp://www.starwars.com/films/star-wars-episode-vii
5th rowhttp://www.furious7.com/

Common Values

ValueCountFrequency (%)
http://www.thehungergames.movie/ 4
 
0.3%
http://www.missionimpossible.com/ 4
 
0.3%
http://www.thehobbit.com/ 3
 
0.2%
http://www.transformersmovie.com/ 3
 
0.2%
http://disney.go.com/disneypictures/pirates/ 2
 
0.2%
http://www.theamazingspiderman.com 2
 
0.2%
http://www.indianajones.com 2
 
0.2%
http://phantasm.com 2
 
0.2%
http://www.howtotrainyourdragon.com/ 2
 
0.2%
http://www.lordoftherings.net/ 2
 
0.2%
Other values (1256) 1261
98.0%

Length

2023-01-31T08:17:38.250797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
http://www.missionimpossible.com 5
 
0.4%
http://www.thehungergames.movie 4
 
0.3%
http://www.transformersmovie.com 4
 
0.3%
http://www.thehobbit.com 3
 
0.2%
http://www.lordoftherings.net 3
 
0.2%
http://www.ironmanmovie.com 2
 
0.2%
http://www.twilightthemovie.com 2
 
0.2%
http://www.harrypotter.com 2
 
0.2%
http://www.kungfupanda.com 2
 
0.2%
http://stepupmovie.com 2
 
0.2%
Other values (1252) 1258
97.7%

Most occurring characters

ValueCountFrequency (%)
/ 4352
 
9.2%
t 4306
 
9.1%
o 3413
 
7.2%
e 3394
 
7.2%
w 3329
 
7.0%
m 2707
 
5.7%
. 2558
 
5.4%
h 2323
 
4.9%
i 2309
 
4.9%
c 1862
 
3.9%
Other values (63) 16711
35.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37824
80.0%
Other Punctuation 8246
 
17.4%
Dash Punctuation 473
 
1.0%
Decimal Number 434
 
0.9%
Uppercase Letter 197
 
0.4%
Connector Punctuation 67
 
0.1%
Math Symbol 19
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 4306
11.4%
o 3413
 
9.0%
e 3394
 
9.0%
w 3329
 
8.8%
m 2707
 
7.2%
h 2323
 
6.1%
i 2309
 
6.1%
c 1862
 
4.9%
p 1791
 
4.7%
r 1739
 
4.6%
Other values (16) 10651
28.2%
Uppercase Letter
ValueCountFrequency (%)
E 17
 
8.6%
T 17
 
8.6%
M 16
 
8.1%
S 16
 
8.1%
A 15
 
7.6%
D 14
 
7.1%
N 12
 
6.1%
L 10
 
5.1%
O 9
 
4.6%
G 8
 
4.1%
Other values (13) 63
32.0%
Decimal Number
ValueCountFrequency (%)
2 89
20.5%
1 73
16.8%
0 63
14.5%
3 62
14.3%
9 30
 
6.9%
4 30
 
6.9%
7 28
 
6.5%
8 22
 
5.1%
5 22
 
5.1%
6 15
 
3.5%
Other Punctuation
ValueCountFrequency (%)
/ 4352
52.8%
. 2558
31.0%
: 1287
 
15.6%
# 18
 
0.2%
? 13
 
0.2%
% 9
 
0.1%
& 6
 
0.1%
, 2
 
< 0.1%
! 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 473
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 67
100.0%
Math Symbol
ValueCountFrequency (%)
= 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38021
80.4%
Common 9243
 
19.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 4306
 
11.3%
o 3413
 
9.0%
e 3394
 
8.9%
w 3329
 
8.8%
m 2707
 
7.1%
h 2323
 
6.1%
i 2309
 
6.1%
c 1862
 
4.9%
p 1791
 
4.7%
r 1739
 
4.6%
Other values (39) 10848
28.5%
Common
ValueCountFrequency (%)
/ 4352
47.1%
. 2558
27.7%
: 1287
 
13.9%
- 473
 
5.1%
2 89
 
1.0%
1 73
 
0.8%
_ 67
 
0.7%
0 63
 
0.7%
3 62
 
0.7%
9 30
 
0.3%
Other values (14) 189
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 4352
 
9.2%
t 4306
 
9.1%
o 3413
 
7.2%
e 3394
 
7.2%
w 3329
 
7.0%
m 2707
 
5.7%
. 2558
 
5.4%
h 2323
 
4.9%
i 2309
 
4.9%
c 1862
 
3.9%
Other values (63) 16711
35.4%

director
Categorical

Distinct789
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
John Carpenter
 
12
Steven Spielberg
 
11
Steven Soderbergh
 
10
Robert Zemeckis
 
8
Clint Eastwood
 
8
Other values (784)
1238 

Length

Max length79
Median length40
Mean length14.3885
Min length3

Characters and Unicode

Total characters18518
Distinct characters69
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique535 ?
Unique (%)41.6%

Sample

1st rowColin Trevorrow
2nd rowGeorge Miller
3rd rowRobert Schwentke
4th rowJ.J. Abrams
5th rowJames Wan

Common Values

ValueCountFrequency (%)
John Carpenter 12
 
0.9%
Steven Spielberg 11
 
0.9%
Steven Soderbergh 10
 
0.8%
Robert Zemeckis 8
 
0.6%
Clint Eastwood 8
 
0.6%
Ridley Scott 8
 
0.6%
Peter Jackson 8
 
0.6%
Ron Howard 7
 
0.5%
Christopher Nolan 7
 
0.5%
Paul W.S. Anderson 7
 
0.5%
Other values (779) 1201
93.3%

Length

2023-01-31T08:17:38.366164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
john 57
 
2.0%
david 50
 
1.8%
peter 32
 
1.1%
steven 28
 
1.0%
paul 26
 
0.9%
michael 24
 
0.9%
robert 22
 
0.8%
james 20
 
0.7%
rob 20
 
0.7%
martin 17
 
0.6%
Other values (1179) 2488
89.4%

Most occurring characters

ValueCountFrequency (%)
e 1760
 
9.5%
1498
 
8.1%
a 1395
 
7.5%
n 1335
 
7.2%
r 1300
 
7.0%
o 1083
 
5.8%
i 1052
 
5.7%
l 835
 
4.5%
t 682
 
3.7%
s 652
 
3.5%
Other values (59) 6926
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13777
74.4%
Uppercase Letter 3000
 
16.2%
Space Separator 1498
 
8.1%
Math Symbol 126
 
0.7%
Other Punctuation 88
 
0.5%
Dash Punctuation 10
 
0.1%
Currency Symbol 8
 
< 0.1%
Other Number 4
 
< 0.1%
Other Symbol 3
 
< 0.1%
Final Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 319
 
10.6%
J 264
 
8.8%
M 249
 
8.3%
R 204
 
6.8%
C 203
 
6.8%
B 183
 
6.1%
D 170
 
5.7%
G 167
 
5.6%
A 161
 
5.4%
L 140
 
4.7%
Other values (18) 940
31.3%
Lowercase Letter
ValueCountFrequency (%)
e 1760
12.8%
a 1395
10.1%
n 1335
9.7%
r 1300
9.4%
o 1083
 
7.9%
i 1052
 
7.6%
l 835
 
6.1%
t 682
 
5.0%
s 652
 
4.7%
h 519
 
3.8%
Other values (16) 3164
23.0%
Other Punctuation
ValueCountFrequency (%)
. 62
70.5%
¡ 12
 
13.6%
8
 
9.1%
' 4
 
4.5%
1
 
1.1%
§ 1
 
1.1%
Math Symbol
ValueCountFrequency (%)
| 124
98.4%
± 2
 
1.6%
Space Separator
ValueCountFrequency (%)
1498
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Currency Symbol
ValueCountFrequency (%)
¥ 8
100.0%
Other Number
ValueCountFrequency (%)
³ 4
100.0%
Other Symbol
ValueCountFrequency (%)
© 3
100.0%
Final Punctuation
ValueCountFrequency (%)
» 2
100.0%
Format
ValueCountFrequency (%)
­ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16777
90.6%
Common 1741
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1760
 
10.5%
a 1395
 
8.3%
n 1335
 
8.0%
r 1300
 
7.7%
o 1083
 
6.5%
i 1052
 
6.3%
l 835
 
5.0%
t 682
 
4.1%
s 652
 
3.9%
h 519
 
3.1%
Other values (44) 6164
36.7%
Common
ValueCountFrequency (%)
1498
86.0%
| 124
 
7.1%
. 62
 
3.6%
¡ 12
 
0.7%
- 10
 
0.6%
8
 
0.5%
¥ 8
 
0.5%
³ 4
 
0.2%
' 4
 
0.2%
© 3
 
0.2%
Other values (5) 8
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18432
99.5%
None 85
 
0.5%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1760
 
9.5%
1498
 
8.1%
a 1395
 
7.6%
n 1335
 
7.2%
r 1300
 
7.1%
o 1083
 
5.9%
i 1052
 
5.7%
l 835
 
4.5%
t 682
 
3.7%
s 652
 
3.5%
Other values (47) 6840
37.1%
None
ValueCountFrequency (%)
à 42
49.4%
¡ 12
 
14.1%
8
 
9.4%
¥ 8
 
9.4%
³ 4
 
4.7%
© 3
 
3.5%
» 2
 
2.4%
± 2
 
2.4%
­ 2
 
2.4%
§ 1
 
1.2%
Punctuation
ValueCountFrequency (%)
1
100.0%

tagline
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1283
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
The only way out is down.
 
2
Love is a force of nature.
 
2
Evil will rise.
 
2
One ordinary couple. One little white lie.
 
2
Where There Are Gods, There Are Monsters.
 
1
Other values (1278)
1278 

Length

Max length286
Median length96
Mean length37.982906
Min length3

Characters and Unicode

Total characters48884
Distinct characters91
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1279 ?
Unique (%)99.4%

Sample

1st rowThe park is open.
2nd rowWhat a Lovely Day.
3rd rowOne Choice Can Destroy You
4th rowEvery generation has a story.
5th rowVengeance Hits Home

Common Values

ValueCountFrequency (%)
The only way out is down. 2
 
0.2%
Love is a force of nature. 2
 
0.2%
Evil will rise. 2
 
0.2%
One ordinary couple. One little white lie. 2
 
0.2%
Where There Are Gods, There Are Monsters. 1
 
0.1%
2 Guns, 1 Bank. 1
 
0.1%
This is not a game. 1
 
0.1%
Remember Philly! 1
 
0.1%
Yippee Ki-Yay Mother Russia 1
 
0.1%
Based on the true case files of the Warrens 1
 
0.1%
Other values (1273) 1273
98.9%

Length

2023-01-31T08:17:38.486427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 553
 
6.1%
a 309
 
3.4%
is 217
 
2.4%
to 186
 
2.1%
you 170
 
1.9%
of 168
 
1.9%
in 122
 
1.3%
one 109
 
1.2%
it 90
 
1.0%
and 75
 
0.8%
Other values (2027) 7059
77.9%

Most occurring characters

ValueCountFrequency (%)
7778
15.9%
e 5218
 
10.7%
o 3034
 
6.2%
t 3008
 
6.2%
a 2544
 
5.2%
n 2473
 
5.1%
i 2355
 
4.8%
r 2339
 
4.8%
s 2298
 
4.7%
h 1847
 
3.8%
Other values (81) 15990
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35588
72.8%
Space Separator 7778
 
15.9%
Uppercase Letter 3036
 
6.2%
Other Punctuation 2242
 
4.6%
Decimal Number 172
 
0.4%
Dash Punctuation 38
 
0.1%
Currency Symbol 13
 
< 0.1%
Other Symbol 9
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5218
14.7%
o 3034
 
8.5%
t 3008
 
8.5%
a 2544
 
7.1%
n 2473
 
6.9%
i 2355
 
6.6%
r 2339
 
6.6%
s 2298
 
6.5%
h 1847
 
5.2%
l 1462
 
4.1%
Other values (24) 9010
25.3%
Uppercase Letter
ValueCountFrequency (%)
T 427
14.1%
A 262
 
8.6%
S 205
 
6.8%
W 201
 
6.6%
I 198
 
6.5%
H 188
 
6.2%
B 177
 
5.8%
O 141
 
4.6%
N 134
 
4.4%
L 129
 
4.2%
Other values (15) 974
32.1%
Other Punctuation
ValueCountFrequency (%)
. 1554
69.3%
' 311
 
13.9%
, 213
 
9.5%
? 80
 
3.6%
! 71
 
3.2%
: 5
 
0.2%
% 3
 
0.1%
* 1
 
< 0.1%
# 1
 
< 0.1%
& 1
 
< 0.1%
Other values (2) 2
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 62
36.0%
1 29
16.9%
2 18
 
10.5%
7 16
 
9.3%
9 13
 
7.6%
5 10
 
5.8%
3 8
 
4.7%
8 8
 
4.7%
4 4
 
2.3%
6 4
 
2.3%
Currency Symbol
ValueCountFrequency (%)
11
84.6%
$ 2
 
15.4%
Other Symbol
ValueCountFrequency (%)
¦ 5
55.6%
4
44.4%
Open Punctuation
ValueCountFrequency (%)
( 3
75.0%
1
 
25.0%
Space Separator
ValueCountFrequency (%)
7778
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Modifier Letter
ValueCountFrequency (%)
ˆ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38624
79.0%
Common 10260
 
21.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5218
13.5%
o 3034
 
7.9%
t 3008
 
7.8%
a 2544
 
6.6%
n 2473
 
6.4%
i 2355
 
6.1%
r 2339
 
6.1%
s 2298
 
5.9%
h 1847
 
4.8%
l 1462
 
3.8%
Other values (49) 12046
31.2%
Common
ValueCountFrequency (%)
7778
75.8%
. 1554
 
15.1%
' 311
 
3.0%
, 213
 
2.1%
? 80
 
0.8%
! 71
 
0.7%
0 62
 
0.6%
- 38
 
0.4%
1 29
 
0.3%
2 18
 
0.2%
Other values (22) 106
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48843
99.9%
None 23
 
< 0.1%
Currency Symbols 11
 
< 0.1%
Letterlike Symbols 4
 
< 0.1%
Punctuation 2
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7778
15.9%
e 5218
 
10.7%
o 3034
 
6.2%
t 3008
 
6.2%
a 2544
 
5.2%
n 2473
 
5.1%
i 2355
 
4.8%
r 2339
 
4.8%
s 2298
 
4.7%
h 1847
 
3.8%
Other values (66) 15949
32.7%
Currency Symbols
ValueCountFrequency (%)
11
100.0%
None
ValueCountFrequency (%)
â 9
39.1%
¦ 5
21.7%
è 2
 
8.7%
Ž 1
 
4.3%
ž 1
 
4.3%
š 1
 
4.3%
ç 1
 
4.3%
å 1
 
4.3%
œ 1
 
4.3%
æ 1
 
4.3%
Letterlike Symbols
ValueCountFrequency (%)
4
100.0%
Modifier Letters
ValueCountFrequency (%)
ˆ 1
100.0%
Punctuation
ValueCountFrequency (%)
1
50.0%
1
50.0%

keywords
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1272
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
duringcreditsstinger
 
6
woman director
 
4
aftercreditsstinger
 
3
aftercreditsstinger|duringcreditsstinger
 
2
independent film
 
2
Other values (1267)
1270 

Length

Max length131
Median length81
Mean length48.500389
Min length3

Characters and Unicode

Total characters62420
Distinct characters50
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1264 ?
Unique (%)98.2%

Sample

1st rowmonster|dna|tyrannosaurus rex|velociraptor|island
2nd rowfuture|chase|post-apocalyptic|dystopia|australia
3rd rowbased on novel|revolution|dystopia|sequel|dystopic future
4th rowandroid|spaceship|jedi|space opera|3d
5th rowcar race|speed|revenge|suspense|car

Common Values

ValueCountFrequency (%)
duringcreditsstinger 6
 
0.5%
woman director 4
 
0.3%
aftercreditsstinger 3
 
0.2%
aftercreditsstinger|duringcreditsstinger 2
 
0.2%
independent film 2
 
0.2%
sequel 2
 
0.2%
elves|dwarves|orcs|middle-earth (tolkien)|hobbits 2
 
0.2%
independent film|woman director 2
 
0.2%
undercover|undercover agent|based on comic book|number in title|money 1
 
0.1%
angel|vampire|werewolf|warlock|downworlder 1
 
0.1%
Other values (1262) 1262
98.1%

Length

2023-01-31T08:17:38.600549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
on 124
 
3.0%
of 107
 
2.6%
and 54
 
1.3%
based 48
 
1.2%
the 43
 
1.1%
in 39
 
1.0%
sister 25
 
0.6%
new 24
 
0.6%
brother 23
 
0.6%
female 21
 
0.5%
Other values (2912) 3574
87.6%

Most occurring characters

ValueCountFrequency (%)
e 5903
 
9.5%
i 4762
 
7.6%
a 4702
 
7.5%
| 4605
 
7.4%
r 4484
 
7.2%
n 3980
 
6.4%
o 3968
 
6.4%
t 3750
 
6.0%
s 3724
 
6.0%
2794
 
4.5%
Other values (40) 19748
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54756
87.7%
Math Symbol 4605
 
7.4%
Space Separator 2797
 
4.5%
Dash Punctuation 88
 
0.1%
Decimal Number 76
 
0.1%
Other Punctuation 72
 
0.1%
Uppercase Letter 9
 
< 0.1%
Close Punctuation 6
 
< 0.1%
Open Punctuation 6
 
< 0.1%
Other Symbol 3
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5903
10.8%
i 4762
 
8.7%
a 4702
 
8.6%
r 4484
 
8.2%
n 3980
 
7.3%
o 3968
 
7.2%
t 3750
 
6.8%
s 3724
 
6.8%
l 2582
 
4.7%
c 2241
 
4.1%
Other values (16) 14660
26.8%
Decimal Number
ValueCountFrequency (%)
3 20
26.3%
1 14
18.4%
9 13
17.1%
0 12
15.8%
7 10
13.2%
2 3
 
3.9%
5 2
 
2.6%
4 1
 
1.3%
6 1
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 36
50.0%
' 35
48.6%
· 1
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
à 4
44.4%
 3
33.3%
Î 2
22.2%
Space Separator
ValueCountFrequency (%)
2794
99.9%
  3
 
0.1%
Math Symbol
ValueCountFrequency (%)
| 4605
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 88
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Other Symbol
ValueCountFrequency (%)
© 3
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%
Other Number
ValueCountFrequency (%)
³ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54765
87.7%
Common 7655
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5903
10.8%
i 4762
 
8.7%
a 4702
 
8.6%
r 4484
 
8.2%
n 3980
 
7.3%
o 3968
 
7.2%
t 3750
 
6.8%
s 3724
 
6.8%
l 2582
 
4.7%
c 2241
 
4.1%
Other values (19) 14669
26.8%
Common
ValueCountFrequency (%)
| 4605
60.2%
2794
36.5%
- 88
 
1.1%
. 36
 
0.5%
' 35
 
0.5%
3 20
 
0.3%
1 14
 
0.2%
9 13
 
0.2%
0 12
 
0.2%
7 10
 
0.1%
Other values (11) 28
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62402
> 99.9%
None 17
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5903
 
9.5%
i 4762
 
7.6%
a 4702
 
7.5%
| 4605
 
7.4%
r 4484
 
7.2%
n 3980
 
6.4%
o 3968
 
6.4%
t 3750
 
6.0%
s 3724
 
6.0%
2794
 
4.5%
Other values (32) 19730
31.6%
None
ValueCountFrequency (%)
à 4
23.5%
© 3
17.6%
 3
17.6%
  3
17.6%
Î 2
11.8%
· 1
 
5.9%
³ 1
 
5.9%
Punctuation
ValueCountFrequency (%)
1
100.0%

overview
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct1287
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
Twenty-two years after the events of Jurassic Park, Isla Nublar now features a fully functioning dinosaur theme park, Jurassic World, as originally envisioned by John Hammond.
 
1
The Umbrella Corporation’s deadly T-virus continues to ravage the Earth, transforming the global population into legions of the flesh eating Undead. The human race’s last and only hope, Alice, awakens in the heart of Umbrella’s most clandestine operations facility and unveils more of her mysterious past as she delves further into the complex. Without a safe haven, Alice continues to hunt those responsible for the outbreak; a chase that takes her from Tokyo to New York, Washington, D.C. and Moscow, culminating in a mind-blowing revelation that will force her to rethink everything that she once thought to be true. Aided by new found allies and familiar friends, Alice must fight to survive long enough to escape a hostile world on the brink of oblivion. The countdown has begun.
 
1
A DEA agent and an undercover Naval Intelligence officer who have been tasked with investigating one another find they have been set up by the mob -- the very organization the two men believe they have been stealing money from.
 
1
Based on the classic novel by Orson Scott Card, Ender's Game is the story of the Earth's most gifted children training to defend their homeplanet in the space wars of the future.
 
1
Life for former United Nations investigator Gerry Lane and his family seems content. Suddenly, the world is plagued by a mysterious infection turning whole human populations into rampaging mindless zombies. After barely escaping the chaos, Lane is persuaded to go on a mission to investigate this disease. What follows is a perilous trek around the world where Lane must brave horrific dangers and long odds to find answers before human civilization falls.
 
1
Other values (1282)
1282 

Length

Max length1000
Median length481
Mean length311.14141
Min length58

Characters and Unicode

Total characters400439
Distinct characters99
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1287 ?
Unique (%)100.0%

Sample

1st rowTwenty-two years after the events of Jurassic Park, Isla Nublar now features a fully functioning dinosaur theme park, Jurassic World, as originally envisioned by John Hammond.
2nd rowAn apocalyptic story set in the furthest reaches of our planet, in a stark desert landscape where humanity is broken, and most everyone is crazed fighting for the necessities of life. Within this world exist two rebels on the run who just might be able to restore order. There's Max, a man of action and a man of few words, who seeks peace of mind following the loss of his wife and child in the aftermath of the chaos. And Furiosa, a woman of action and a woman who believes her path to survival may be achieved if she can make it across the desert back to her childhood homeland.
3rd rowBeatrice Prior must confront her inner demons and continue her fight against a powerful alliance which threatens to tear her society apart.
4th rowThirty years after defeating the Galactic Empire, Han Solo and his allies face a new threat from the evil Kylo Ren and his army of Stormtroopers.
5th rowDeckard Shaw seeks revenge against Dominic Toretto and his family for his comatose brother.

Common Values

ValueCountFrequency (%)
Twenty-two years after the events of Jurassic Park, Isla Nublar now features a fully functioning dinosaur theme park, Jurassic World, as originally envisioned by John Hammond. 1
 
0.1%
The Umbrella Corporation’s deadly T-virus continues to ravage the Earth, transforming the global population into legions of the flesh eating Undead. The human race’s last and only hope, Alice, awakens in the heart of Umbrella’s most clandestine operations facility and unveils more of her mysterious past as she delves further into the complex. Without a safe haven, Alice continues to hunt those responsible for the outbreak; a chase that takes her from Tokyo to New York, Washington, D.C. and Moscow, culminating in a mind-blowing revelation that will force her to rethink everything that she once thought to be true. Aided by new found allies and familiar friends, Alice must fight to survive long enough to escape a hostile world on the brink of oblivion. The countdown has begun. 1
 
0.1%
A DEA agent and an undercover Naval Intelligence officer who have been tasked with investigating one another find they have been set up by the mob -- the very organization the two men believe they have been stealing money from. 1
 
0.1%
Based on the classic novel by Orson Scott Card, Ender's Game is the story of the Earth's most gifted children training to defend their homeplanet in the space wars of the future. 1
 
0.1%
Life for former United Nations investigator Gerry Lane and his family seems content. Suddenly, the world is plagued by a mysterious infection turning whole human populations into rampaging mindless zombies. After barely escaping the chaos, Lane is persuaded to go on a mission to investigate this disease. What follows is a perilous trek around the world where Lane must brave horrific dangers and long odds to find answers before human civilization falls. 1
 
0.1%
Iconoclastic, take-no-prisoners cop John McClane, finds himself for the first time on foreign soil after traveling to Moscow to help his wayward son Jack - unaware that Jack is really a highly-trained CIA operative out to stop a nuclear weapons heist. With the Russian underworld in pursuit, and battling a countdown to war, the two McClanes discover that their opposing methods make them unstoppable heroes. 1
 
0.1%
Paranormal investigators Ed and Lorraine Warren work to help a family terrorized by a dark presence in their farmhouse. Forced to confront a powerful entity, the Warrens find themselves caught in the most terrifying case of their lives. 1
 
0.1%
Betrayed by his own kind and left for dead on a desolate planet, Riddick fights for survival against alien predators and becomes more powerful and dangerous than ever before. Soon bounty hunters from throughout the galaxy descend on Riddick only to find themselves pawns in his greater scheme for revenge. With his enemies right where he wants them, Riddick unleashes a vicious attack of vengeance before returning to his home planet of Furya to save it from destruction. 1
 
0.1%
Gru is recruited by the Anti-Villain League to help deal with a powerful new super criminal. 1
 
0.1%
In the not so distant future, Theodore, a lonely writer purchases a newly developed operating system designed to meet the user's every needs. To Theordore's surprise, a romantic relationship develops between him and his operating system. This unconventional love story blends science fiction and romance in a sweet tale that explores the nature of love and the ways that technology isolates and connects us all. 1
 
0.1%
Other values (1277) 1277
99.2%

Length

2023-01-31T08:17:38.716801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 3906
 
5.7%
a 2776
 
4.1%
to 2192
 
3.2%
and 2011
 
3.0%
of 1856
 
2.7%
in 1198
 
1.8%
his 1016
 
1.5%
is 873
 
1.3%
with 642
 
0.9%
her 590
 
0.9%
Other values (11513) 51039
74.9%

Most occurring characters

ValueCountFrequency (%)
66850
16.7%
e 38852
 
9.7%
t 26765
 
6.7%
a 25985
 
6.5%
n 23189
 
5.8%
o 22910
 
5.7%
i 22848
 
5.7%
r 21357
 
5.3%
s 21171
 
5.3%
h 16776
 
4.2%
Other values (89) 113736
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 311406
77.8%
Space Separator 66854
 
16.7%
Uppercase Letter 10726
 
2.7%
Other Punctuation 8239
 
2.1%
Dash Punctuation 1190
 
0.3%
Decimal Number 1052
 
0.3%
Currency Symbol 286
 
0.1%
Open Punctuation 202
 
0.1%
Close Punctuation 202
 
0.1%
Other Symbol 161
 
< 0.1%
Other values (4) 121
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 38852
12.5%
t 26765
 
8.6%
a 25985
 
8.3%
n 23189
 
7.4%
o 22910
 
7.4%
i 22848
 
7.3%
r 21357
 
6.9%
s 21171
 
6.8%
h 16776
 
5.4%
l 13212
 
4.2%
Other values (18) 78341
25.2%
Uppercase Letter
ValueCountFrequency (%)
A 1179
 
11.0%
B 856
 
8.0%
T 831
 
7.7%
S 788
 
7.3%
W 620
 
5.8%
C 618
 
5.8%
M 618
 
5.8%
H 500
 
4.7%
D 480
 
4.5%
J 468
 
4.4%
Other values (18) 3768
35.1%
Other Punctuation
ValueCountFrequency (%)
, 3696
44.9%
. 3153
38.3%
' 932
 
11.3%
" 231
 
2.8%
: 85
 
1.0%
; 53
 
0.6%
? 49
 
0.6%
! 18
 
0.2%
/ 9
 
0.1%
& 6
 
0.1%
Other values (5) 7
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 240
22.8%
1 221
21.0%
9 143
13.6%
2 120
11.4%
5 65
 
6.2%
8 63
 
6.0%
7 59
 
5.6%
4 51
 
4.8%
3 50
 
4.8%
6 40
 
3.8%
Other Symbol
ValueCountFrequency (%)
121
75.2%
© 21
 
13.0%
¦ 17
 
10.6%
® 2
 
1.2%
Modifier Symbol
ValueCountFrequency (%)
˜ 11
84.6%
¯ 1
 
7.7%
´ 1
 
7.7%
Space Separator
ValueCountFrequency (%)
66850
> 99.9%
  4
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
277
96.9%
$ 9
 
3.1%
Other Number
ValueCountFrequency (%)
¹ 1
50.0%
³ 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 1190
100.0%
Open Punctuation
ValueCountFrequency (%)
( 202
100.0%
Close Punctuation
ValueCountFrequency (%)
) 202
100.0%
Initial Punctuation
ValueCountFrequency (%)
81
100.0%
Final Punctuation
ValueCountFrequency (%)
25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 322132
80.4%
Common 78307
 
19.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 38852
12.1%
t 26765
 
8.3%
a 25985
 
8.1%
n 23189
 
7.2%
o 22910
 
7.1%
i 22848
 
7.1%
r 21357
 
6.6%
s 21171
 
6.6%
h 16776
 
5.2%
l 13212
 
4.1%
Other values (46) 89067
27.6%
Common
ValueCountFrequency (%)
66850
85.4%
, 3696
 
4.7%
. 3153
 
4.0%
- 1190
 
1.5%
' 932
 
1.2%
277
 
0.4%
0 240
 
0.3%
" 231
 
0.3%
1 221
 
0.3%
( 202
 
0.3%
Other values (33) 1315
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 399550
99.8%
None 372
 
0.1%
Currency Symbols 277
 
0.1%
Letterlike Symbols 121
 
< 0.1%
Punctuation 108
 
< 0.1%
Modifier Letters 11
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
66850
16.7%
e 38852
 
9.7%
t 26765
 
6.7%
a 25985
 
6.5%
n 23189
 
5.8%
o 22910
 
5.7%
i 22848
 
5.7%
r 21357
 
5.3%
s 21171
 
5.3%
h 16776
 
4.2%
Other values (69) 112847
28.2%
None
ValueCountFrequency (%)
â 277
74.5%
à 25
 
6.7%
© 21
 
5.6%
¦ 17
 
4.6%
œ 10
 
2.7%
 9
 
2.4%
  4
 
1.1%
® 2
 
0.5%
· 2
 
0.5%
¹ 1
 
0.3%
Other values (4) 4
 
1.1%
Currency Symbols
ValueCountFrequency (%)
277
100.0%
Letterlike Symbols
ValueCountFrequency (%)
121
100.0%
Punctuation
ValueCountFrequency (%)
81
75.0%
25
 
23.1%
2
 
1.9%
Modifier Letters
ValueCountFrequency (%)
˜ 11
100.0%

runtime
Real number (ℝ)

Distinct102
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.2735
Minimum63
Maximum201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-01-31T08:17:38.816709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile87
Q197
median107
Q3121
95-th percentile145
Maximum201
Range138
Interquartile range (IQR)24

Descriptive statistics

Standard deviation18.811369
Coefficient of variation (CV)0.17058829
Kurtosis1.7660891
Mean110.2735
Median Absolute Deviation (MAD)12
Skewness1.0789244
Sum141922
Variance353.86759
MonotonicityNot monotonic
2023-01-31T08:17:38.916693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 44
 
3.4%
90 39
 
3.0%
102 34
 
2.6%
97 34
 
2.6%
109 33
 
2.6%
108 33
 
2.6%
106 33
 
2.6%
98 30
 
2.3%
95 30
 
2.3%
107 30
 
2.3%
Other values (92) 947
73.6%
ValueCountFrequency (%)
63 1
 
0.1%
75 1
 
0.1%
76 1
 
0.1%
77 1
 
0.1%
79 3
 
0.2%
80 6
0.5%
81 6
0.5%
82 6
0.5%
83 8
0.6%
84 8
0.6%
ValueCountFrequency (%)
201 1
0.1%
195 1
0.1%
194 1
0.1%
189 1
0.1%
188 1
0.1%
180 1
0.1%
179 1
0.1%
178 2
0.2%
175 1
0.1%
172 1
0.1%

genres
Categorical

Distinct496
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
Drama
 
76
Comedy
 
69
Drama|Romance
 
37
Comedy|Romance
 
30
Comedy|Drama|Romance
 
29
Other values (491)
1046 

Length

Max length49
Median length41
Mean length20.586636
Min length5

Characters and Unicode

Total characters26495
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique327 ?
Unique (%)25.4%

Sample

1st rowAction|Adventure|Science Fiction|Thriller
2nd rowAction|Adventure|Science Fiction|Thriller
3rd rowAdventure|Science Fiction|Thriller
4th rowAction|Adventure|Science Fiction|Fantasy
5th rowAction|Crime|Thriller

Common Values

ValueCountFrequency (%)
Drama 76
 
5.9%
Comedy 69
 
5.4%
Drama|Romance 37
 
2.9%
Comedy|Romance 30
 
2.3%
Comedy|Drama|Romance 29
 
2.3%
Horror|Thriller 28
 
2.2%
Comedy|Drama 23
 
1.8%
Adventure|Action|Thriller 20
 
1.6%
Drama|Thriller 18
 
1.4%
Horror 17
 
1.3%
Other values (486) 940
73.0%

Length

2023-01-31T08:17:39.032280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fiction 112
 
7.5%
drama 76
 
5.1%
comedy 69
 
4.6%
science 38
 
2.5%
drama|romance 37
 
2.5%
comedy|romance 30
 
2.0%
comedy|drama|romance 29
 
1.9%
horror|thriller 28
 
1.9%
fiction|thriller 26
 
1.7%
comedy|drama 23
 
1.5%
Other values (470) 1028
68.7%

Most occurring characters

ValueCountFrequency (%)
r 2434
 
9.2%
e 2354
 
8.9%
| 2167
 
8.2%
i 2087
 
7.9%
a 1892
 
7.1%
n 1725
 
6.5%
o 1671
 
6.3%
m 1624
 
6.1%
t 1344
 
5.1%
c 1291
 
4.9%
Other values (19) 7906
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20456
77.2%
Uppercase Letter 3663
 
13.8%
Math Symbol 2167
 
8.2%
Space Separator 209
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 2434
11.9%
e 2354
11.5%
i 2087
10.2%
a 1892
9.2%
n 1725
8.4%
o 1671
8.2%
m 1624
7.9%
t 1344
6.6%
c 1291
6.3%
y 977
 
4.8%
Other values (7) 3057
14.9%
Uppercase Letter
ValueCountFrequency (%)
A 820
22.4%
C 607
16.6%
D 550
15.0%
F 527
14.4%
T 399
10.9%
S 209
 
5.7%
R 196
 
5.4%
H 174
 
4.8%
M 136
 
3.7%
W 45
 
1.2%
Math Symbol
ValueCountFrequency (%)
| 2167
100.0%
Space Separator
ValueCountFrequency (%)
209
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24119
91.0%
Common 2376
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 2434
 
10.1%
e 2354
 
9.8%
i 2087
 
8.7%
a 1892
 
7.8%
n 1725
 
7.2%
o 1671
 
6.9%
m 1624
 
6.7%
t 1344
 
5.6%
c 1291
 
5.4%
y 977
 
4.1%
Other values (17) 6720
27.9%
Common
ValueCountFrequency (%)
| 2167
91.2%
209
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 2434
 
9.2%
e 2354
 
8.9%
| 2167
 
8.2%
i 2087
 
7.9%
a 1892
 
7.1%
n 1725
 
6.5%
o 1671
 
6.3%
m 1624
 
6.1%
t 1344
 
5.1%
c 1291
 
4.9%
Other values (19) 7906
29.8%

production_companies
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1138
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
Walt Disney Pictures|Pixar Animation Studios
 
12
DreamWorks Animation
 
10
Eon Productions
 
9
Marvel Studios
 
8
Paramount Pictures
 
7
Other values (1133)
1241 

Length

Max length172
Median length104
Mean length60.480186
Min length3

Characters and Unicode

Total characters77838
Distinct characters84
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1081 ?
Unique (%)84.0%

Sample

1st rowUniversal Studios|Amblin Entertainment|Legendary Pictures|Fuji Television Network|Dentsu
2nd rowVillage Roadshow Pictures|Kennedy Miller Productions
3rd rowSummit Entertainment|Mandeville Films|Red Wagon Entertainment|NeoReel
4th rowLucasfilm|Truenorth Productions|Bad Robot
5th rowUniversal Pictures|Original Film|Media Rights Capital|Dentsu|One Race Films

Common Values

ValueCountFrequency (%)
Walt Disney Pictures|Pixar Animation Studios 12
 
0.9%
DreamWorks Animation 10
 
0.8%
Eon Productions 9
 
0.7%
Marvel Studios 8
 
0.6%
Paramount Pictures 7
 
0.5%
New Line Cinema 7
 
0.5%
Universal Pictures 7
 
0.5%
Columbia Pictures 6
 
0.5%
Walt Disney Pictures|Walt Disney Animation Studios 6
 
0.5%
Eon Productions|Metro-Goldwyn-Mayer (MGM) 6
 
0.5%
Other values (1128) 1209
93.9%

Length

2023-01-31T08:17:39.149731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
productions 257
 
3.6%
pictures 208
 
2.9%
films 191
 
2.7%
entertainment 183
 
2.6%
film 166
 
2.3%
universal 109
 
1.5%
columbia 87
 
1.2%
fox 86
 
1.2%
disney 78
 
1.1%
paramount 72
 
1.0%
Other values (2837) 5653
79.7%

Most occurring characters

ValueCountFrequency (%)
i 6092
 
7.8%
e 6077
 
7.8%
5803
 
7.5%
n 5689
 
7.3%
t 5646
 
7.3%
r 5055
 
6.5%
a 4380
 
5.6%
o 4275
 
5.5%
s 3740
 
4.8%
| 2763
 
3.5%
Other values (74) 28318
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57656
74.1%
Uppercase Letter 10671
 
13.7%
Space Separator 5803
 
7.5%
Math Symbol 2789
 
3.6%
Other Punctuation 318
 
0.4%
Decimal Number 287
 
0.4%
Dash Punctuation 110
 
0.1%
Close Punctuation 85
 
0.1%
Open Punctuation 85
 
0.1%
Other Symbol 30
 
< 0.1%
Other values (4) 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 1832
17.2%
F 1130
 
10.6%
C 902
 
8.5%
E 785
 
7.4%
M 700
 
6.6%
S 669
 
6.3%
W 470
 
4.4%
B 468
 
4.4%
D 457
 
4.3%
A 392
 
3.7%
Other values (17) 2866
26.9%
Lowercase Letter
ValueCountFrequency (%)
i 6092
10.6%
e 6077
10.5%
n 5689
9.9%
t 5646
9.8%
r 5055
8.8%
a 4380
 
7.6%
o 4275
 
7.4%
s 3740
 
6.5%
u 2721
 
4.7%
l 2583
 
4.5%
Other values (16) 11398
19.8%
Decimal Number
ValueCountFrequency (%)
0 67
23.3%
2 66
23.0%
1 40
13.9%
4 30
10.5%
3 26
 
9.1%
9 19
 
6.6%
6 16
 
5.6%
8 11
 
3.8%
7 9
 
3.1%
5 3
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 205
64.5%
/ 46
 
14.5%
& 26
 
8.2%
' 17
 
5.3%
, 17
 
5.3%
2
 
0.6%
: 2
 
0.6%
" 2
 
0.6%
1
 
0.3%
Math Symbol
ValueCountFrequency (%)
| 2763
99.1%
+ 24
 
0.9%
± 2
 
0.1%
Space Separator
ValueCountFrequency (%)
5803
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 110
100.0%
Close Punctuation
ValueCountFrequency (%)
) 85
100.0%
Open Punctuation
ValueCountFrequency (%)
( 85
100.0%
Other Symbol
ValueCountFrequency (%)
© 30
100.0%
Currency Symbol
ValueCountFrequency (%)
¤ 1
100.0%
Format
ValueCountFrequency (%)
­ 1
100.0%
Other Number
ValueCountFrequency (%)
³ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
¯ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 68327
87.8%
Common 9511
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6092
 
8.9%
e 6077
 
8.9%
n 5689
 
8.3%
t 5646
 
8.3%
r 5055
 
7.4%
a 4380
 
6.4%
o 4275
 
6.3%
s 3740
 
5.5%
u 2721
 
4.0%
l 2583
 
3.8%
Other values (43) 22069
32.3%
Common
ValueCountFrequency (%)
5803
61.0%
| 2763
29.1%
. 205
 
2.2%
- 110
 
1.2%
) 85
 
0.9%
( 85
 
0.9%
0 67
 
0.7%
2 66
 
0.7%
/ 46
 
0.5%
1 40
 
0.4%
Other values (21) 241
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77760
99.9%
None 76
 
0.1%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 6092
 
7.8%
e 6077
 
7.8%
5803
 
7.5%
n 5689
 
7.3%
t 5646
 
7.3%
r 5055
 
6.5%
a 4380
 
5.6%
o 4275
 
5.5%
s 3740
 
4.8%
| 2763
 
3.6%
Other values (65) 28240
36.3%
None
ValueCountFrequency (%)
à 39
51.3%
© 30
39.5%
± 2
 
2.6%
¤ 1
 
1.3%
­ 1
 
1.3%
1
 
1.3%
³ 1
 
1.3%
¯ 1
 
1.3%
Punctuation
ValueCountFrequency (%)
2
100.0%

release_date
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1080
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
2011-09-30
 
5
2014-12-25
 
5
2011-09-16
 
4
2009-03-19
 
4
2007-09-06
 
4
Other values (1075)
1265 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters12870
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique906 ?
Unique (%)70.4%

Sample

1st row2015-06-09
2nd row2015-05-13
3rd row2015-03-18
4th row2015-12-15
5th row2015-04-01

Common Values

ValueCountFrequency (%)
2011-09-30 5
 
0.4%
2014-12-25 5
 
0.4%
2011-09-16 4
 
0.3%
2009-03-19 4
 
0.3%
2007-09-06 4
 
0.3%
2011-04-08 3
 
0.2%
2010-09-11 3
 
0.2%
2012-03-12 3
 
0.2%
2012-09-07 3
 
0.2%
2015-11-20 3
 
0.2%
Other values (1070) 1250
97.1%

Length

2023-01-31T08:17:39.249636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2011-09-30 5
 
0.4%
2014-12-25 5
 
0.4%
2011-09-16 4
 
0.3%
2009-03-19 4
 
0.3%
2007-09-06 4
 
0.3%
2005-09-16 3
 
0.2%
2011-09-22 3
 
0.2%
2009-10-10 3
 
0.2%
2012-01-19 3
 
0.2%
2015-12-25 3
 
0.2%
Other values (1070) 1250
97.1%

Most occurring characters

ValueCountFrequency (%)
0 3410
26.5%
- 2574
20.0%
1 2092
16.3%
2 2012
15.6%
9 640
 
5.0%
5 399
 
3.1%
3 386
 
3.0%
8 355
 
2.8%
7 341
 
2.6%
4 331
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10296
80.0%
Dash Punctuation 2574
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3410
33.1%
1 2092
20.3%
2 2012
19.5%
9 640
 
6.2%
5 399
 
3.9%
3 386
 
3.7%
8 355
 
3.4%
7 341
 
3.3%
4 331
 
3.2%
6 330
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 2574
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12870
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3410
26.5%
- 2574
20.0%
1 2092
16.3%
2 2012
15.6%
9 640
 
5.0%
5 399
 
3.1%
3 386
 
3.0%
8 355
 
2.8%
7 341
 
2.6%
4 331
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3410
26.5%
- 2574
20.0%
1 2092
16.3%
2 2012
15.6%
9 640
 
5.0%
5 399
 
3.1%
3 386
 
3.0%
8 355
 
2.8%
7 341
 
2.6%
4 331
 
2.6%

vote_count
Real number (ℝ)

Distinct894
Distinct (%)69.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean947.26651
Minimum10
Maximum9767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-01-31T08:17:39.332893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile42
Q1179
median439
Q31173
95-th percentile3557.5
Maximum9767
Range9757
Interquartile range (IQR)994

Descriptive statistics

Standard deviation1255.4762
Coefficient of variation (CV)1.3253675
Kurtosis8.6303039
Mean947.26651
Median Absolute Deviation (MAD)341
Skewness2.580723
Sum1219132
Variance1576220.5
MonotonicityNot monotonic
2023-01-31T08:17:39.432784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130 7
 
0.5%
63 6
 
0.5%
78 6
 
0.5%
205 6
 
0.5%
423 6
 
0.5%
12 5
 
0.4%
51 5
 
0.4%
58 5
 
0.4%
96 5
 
0.4%
151 5
 
0.4%
Other values (884) 1231
95.6%
ValueCountFrequency (%)
10 1
 
0.1%
11 2
 
0.2%
12 5
0.4%
13 1
 
0.1%
14 1
 
0.1%
15 1
 
0.1%
16 4
0.3%
18 3
0.2%
19 2
 
0.2%
20 1
 
0.1%
ValueCountFrequency (%)
9767 1
0.1%
8903 1
0.1%
8458 1
0.1%
8432 1
0.1%
7375 1
0.1%
7080 1
0.1%
6882 1
0.1%
6723 1
0.1%
6498 1
0.1%
6417 1
0.1%

vote_average
Real number (ℝ)

Distinct48
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2794872
Minimum2.2
Maximum8.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-01-31T08:17:39.552713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile5
Q15.8
median6.3
Q36.8
95-th percentile7.6
Maximum8.3
Range6.1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7959552
Coefficient of variation (CV)0.12675481
Kurtosis0.53935859
Mean6.2794872
Median Absolute Deviation (MAD)0.5
Skewness-0.3003751
Sum8081.7
Variance0.63354468
MonotonicityNot monotonic
2023-01-31T08:17:39.651735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
6.5 69
 
5.4%
6.3 65
 
5.1%
5.9 65
 
5.1%
6 62
 
4.8%
6.1 62
 
4.8%
6.2 62
 
4.8%
6.6 61
 
4.7%
6.9 59
 
4.6%
5.8 58
 
4.5%
6.4 58
 
4.5%
Other values (38) 666
51.7%
ValueCountFrequency (%)
2.2 1
 
0.1%
3.3 1
 
0.1%
3.4 1
 
0.1%
3.8 4
0.3%
3.9 2
 
0.2%
4 1
 
0.1%
4.2 3
0.2%
4.3 2
 
0.2%
4.4 7
0.5%
4.5 5
0.4%
ValueCountFrequency (%)
8.3 1
 
0.1%
8.2 1
 
0.1%
8.1 3
 
0.2%
8 8
 
0.6%
7.9 9
 
0.7%
7.8 14
1.1%
7.7 14
1.1%
7.6 25
1.9%
7.5 20
1.6%
7.4 17
1.3%

release_year
Real number (ℝ)

Distinct51
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.0171
Minimum1961
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-01-31T08:17:39.749346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1961
5-th percentile1991
Q12005
median2009
Q32011
95-th percentile2015
Maximum2015
Range54
Interquartile range (IQR)6

Descriptive statistics

Standard deviation8.0605033
Coefficient of variation (CV)0.0040161608
Kurtosis8.1056485
Mean2007.0171
Median Absolute Deviation (MAD)3
Skewness-2.5419022
Sum2583031
Variance64.971714
MonotonicityNot monotonic
2023-01-31T08:17:39.864649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2011 156
12.1%
2010 132
10.3%
2009 116
 
9.0%
2015 93
 
7.2%
2007 92
 
7.1%
2012 88
 
6.8%
2008 82
 
6.4%
2014 70
 
5.4%
2006 68
 
5.3%
2013 65
 
5.1%
Other values (41) 325
25.3%
ValueCountFrequency (%)
1961 1
 
0.1%
1962 1
 
0.1%
1963 1
 
0.1%
1964 2
0.2%
1965 1
 
0.1%
1967 1
 
0.1%
1969 1
 
0.1%
1971 4
0.3%
1972 1
 
0.1%
1973 2
0.2%
ValueCountFrequency (%)
2015 93
7.2%
2014 70
5.4%
2013 65
5.1%
2012 88
6.8%
2011 156
12.1%
2010 132
10.3%
2009 116
9.0%
2008 82
6.4%
2007 92
7.1%
2006 68
5.3%

budget_adj
Real number (ℝ)

Distinct835
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54629936
Minimum0.96939804
Maximum4.25 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-01-31T08:17:39.965196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.96939804
5-th percentile2253188.3
Q115191800
median35569267
Q376301250
95-th percentile1.6803223 × 108
Maximum4.25 × 108
Range4.25 × 108
Interquartile range (IQR)61109451

Descriptive statistics

Standard deviation55254628
Coefficient of variation (CV)1.011435
Kurtosis3.7592508
Mean54629936
Median Absolute Deviation (MAD)25449272
Skewness1.7152717
Sum7.0308727 × 1010
Variance3.0530739 × 1015
MonotonicityNot monotonic
2023-01-31T08:17:40.072236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38775921.7 9
 
0.7%
20328008.68 8
 
0.6%
20000000 8
 
0.6%
40656017.36 8
 
0.6%
29081941.28 8
 
0.6%
48469902.13 7
 
0.5%
24234951.06 7
 
0.5%
26291714.57 6
 
0.5%
21033371.65 6
 
0.5%
60767198.03 6
 
0.5%
Other values (825) 1214
94.3%
ValueCountFrequency (%)
0.9693980426 1
0.1%
3 1
0.1%
50.06695621 1
0.1%
76.23003256 1
0.1%
82.43377477 1
0.1%
90.15401796 1
0.1%
7755.184341 1
0.1%
8081.117799 1
0.1%
15775.02874 1
0.1%
16479.76672 1
0.1%
ValueCountFrequency (%)
425000000 1
0.1%
368371256.2 1
0.1%
315500574.8 1
0.1%
271692064.2 1
0.1%
271330494.3 1
0.1%
260000000 1
0.1%
257599886.7 1
0.1%
254100108.5 1
0.1%
250000000 1
0.1%
246933513.2 1
0.1%

revenue_adj
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1287
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.991775 × 108
Minimum43
Maximum2.8271238 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-01-31T08:17:40.164432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile712674.93
Q127648902
median86747696
Q32.3511781 × 108
95-th percentile7.8477998 × 108
Maximum2.8271238 × 109
Range2.8271237 × 109
Interquartile range (IQR)2.074689 × 108

Descriptive statistics

Standard deviation2.9685146 × 108
Coefficient of variation (CV)1.4903865
Kurtosis17.129028
Mean1.991775 × 108
Median Absolute Deviation (MAD)75738109
Skewness3.3456612
Sum2.5634144 × 1011
Variance8.8120791 × 1016
MonotonicityNot monotonic
2023-01-31T08:17:40.265330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1392445893 1
 
0.1%
228089879.1 1
 
0.1%
123500625.3 1
 
0.1%
117506997.8 1
 
0.1%
497843379.2 1
 
0.1%
285166475.4 1
 
0.1%
297658738.2 1
 
0.1%
92046987.94 1
 
0.1%
908665501.9 1
 
0.1%
44322350.23 1
 
0.1%
Other values (1277) 1277
99.2%
ValueCountFrequency (%)
43 1
0.1%
48.3767548 1
0.1%
136.1976582 1
0.1%
233.966449 1
0.1%
333.7797081 1
0.1%
1335.830503 1
0.1%
7425.821572 1
0.1%
13881.76323 1
0.1%
18393.79866 1
0.1%
29706.10748 1
0.1%
ValueCountFrequency (%)
2827123750 1
0.1%
2789712242 1
0.1%
2506405735 1
0.1%
2167324901 1
0.1%
1907005842 1
0.1%
1902723130 1
0.1%
1791694309 1
0.1%
1443191435 1
0.1%
1424626188 1
0.1%
1392445893 1
0.1%

profit
Real number (ℝ)

Distinct1283
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2424095 × 108
Minimum-4.1391243 × 108
Maximum2.5445058 × 109
Zeros0
Zeros (%)0.0%
Negative265
Negative (%)20.6%
Memory size10.2 KiB
2023-01-31T08:17:40.366249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-4.1391243 × 108
5-th percentile-15487781
Q13142641
median45243000
Q31.4700697 × 108
95-th percentile5.6667045 × 108
Maximum2.5445058 × 109
Range2.9584183 × 109
Interquartile range (IQR)1.4386433 × 108

Descriptive statistics

Standard deviation2.183462 × 108
Coefficient of variation (CV)1.7574415
Kurtosis20.909727
Mean1.2424095 × 108
Median Absolute Deviation (MAD)49159401
Skewness3.5496889
Sum1.598981 × 1011
Variance4.7675063 × 1016
MonotonicityNot monotonic
2023-01-31T08:17:40.465478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102000000 2
 
0.2%
44000000 2
 
0.2%
2000000 2
 
0.2%
-14000000 2
 
0.2%
1363528810 1
 
0.1%
24351251 1
 
0.1%
212654182 1
 
0.1%
305000141 1
 
0.1%
60337295 1
 
0.1%
894761885 1
 
0.1%
Other values (1273) 1273
98.9%
ValueCountFrequency (%)
-413912431 1
0.1%
-165710090 1
0.1%
-111007242 1
0.1%
-84540684 1
0.1%
-74010360 1
0.1%
-71331093 1
0.1%
-68351500 1
0.1%
-64926294 1
0.1%
-61900000 1
0.1%
-61403089 1
0.1%
ValueCountFrequency (%)
2544505847 1
0.1%
1868178225 1
0.1%
1645034188 1
0.1%
1363528810 1
0.1%
1316249360 1
0.1%
1299557910 1
0.1%
1202817822 1
0.1%
1125035767 1
0.1%
1124219009 1
0.1%
1082730962 1
0.1%

popularity_level
Categorical

Distinct4
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size10.2 KiB
High
322 
Medium
322 
Moderately High
321 
Low
321 

Length

Max length15
Median length6
Mean length6.9968896
Min length3

Characters and Unicode

Total characters8998
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowHigh
4th rowHigh
5th rowHigh

Common Values

ValueCountFrequency (%)
High 322
25.0%
Medium 322
25.0%
Moderately High 321
24.9%
Low 321
24.9%
(Missing) 1
 
0.1%

Length

2023-01-31T08:17:40.570220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T08:17:40.665949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
high 643
40.0%
medium 322
20.0%
moderately 321
20.0%
low 321
20.0%

Most occurring characters

ValueCountFrequency (%)
i 965
 
10.7%
e 964
 
10.7%
H 643
 
7.1%
g 643
 
7.1%
h 643
 
7.1%
M 643
 
7.1%
d 643
 
7.1%
o 642
 
7.1%
m 322
 
3.6%
u 322
 
3.6%
Other values (8) 2568
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7070
78.6%
Uppercase Letter 1607
 
17.9%
Space Separator 321
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 965
13.6%
e 964
13.6%
g 643
9.1%
h 643
9.1%
d 643
9.1%
o 642
9.1%
m 322
 
4.6%
u 322
 
4.6%
r 321
 
4.5%
a 321
 
4.5%
Other values (4) 1284
18.2%
Uppercase Letter
ValueCountFrequency (%)
H 643
40.0%
M 643
40.0%
L 321
20.0%
Space Separator
ValueCountFrequency (%)
321
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8677
96.4%
Common 321
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 965
11.1%
e 964
11.1%
H 643
 
7.4%
g 643
 
7.4%
h 643
 
7.4%
M 643
 
7.4%
d 643
 
7.4%
o 642
 
7.4%
m 322
 
3.7%
u 322
 
3.7%
Other values (7) 2247
25.9%
Common
ValueCountFrequency (%)
321
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 965
 
10.7%
e 964
 
10.7%
H 643
 
7.1%
g 643
 
7.1%
h 643
 
7.1%
M 643
 
7.1%
d 643
 
7.1%
o 642
 
7.1%
m 322
 
3.6%
u 322
 
3.6%
Other values (8) 2568
28.5%

Interactions

2023-01-31T08:17:35.079442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:23.016070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:24.175282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:25.268283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:26.454858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:27.485429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:28.525922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:29.734223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:30.772665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:31.812815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:32.854512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:34.038787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:35.166729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:23.128123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:24.265235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:25.353488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:26.537466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:27.568667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:28.617019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:29.818614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:30.857215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:31.889319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:32.936352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:34.121961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:35.260032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:23.220046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:24.357172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:25.443110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:26.625042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:27.660755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:28.709424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:29.908499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:30.948107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:31.988157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:33.022233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:34.212476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:35.348864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:23.307793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:24.446100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:25.529264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:26.711466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:27.749908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:28.798272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:29.995162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:31.035201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:32.076315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:33.105877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:34.298466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:35.432872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:23.463581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:24.531306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:25.627742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:26.793050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:27.831692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:28.885054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:30.079159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:31.121020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:32.160000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:33.355469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:34.381430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:35.519346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:23.563436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:24.625712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:25.718333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:26.878729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:27.918576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:28.972390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:30.164963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:31.206809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:32.247369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:33.440749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:34.466714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:35.610896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:23.653411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:24.720043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:25.807987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:26.965320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:28.007749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:29.063645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:30.253279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:31.297224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:32.339444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:33.529000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:34.557150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:35.696700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:23.737997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:24.807672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:25.893815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:27.048799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:28.093037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:29.149888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:30.337639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:31.382918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:32.422164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:33.610807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:34.637824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:35.784724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:23.825233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:24.899062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:26.097688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:27.140292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:28.181619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:29.378875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:30.425124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:31.469153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:32.509791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:33.699243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:34.732563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:35.872734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:23.912301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:24.993068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:26.193569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:27.230367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:28.269802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:29.467896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:30.512878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:31.556486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:32.595451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:33.784157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:34.820183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:35.958017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:23.998457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:25.081790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:26.277948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:27.316106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:28.346233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:29.553588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:30.595894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:31.640426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:32.679382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:33.868777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:34.903607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:36.047373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:24.084991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:25.171942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:26.366200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:27.399868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:28.437984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:29.642060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:30.684211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:31.724057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:32.765444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:33.953572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-31T08:17:34.989836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-31T08:17:40.749558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Unnamed: 0idpopularitybudgetrevenueruntimevote_countvote_averagerelease_yearbudget_adjrevenue_adjprofitpopularity_level
Unnamed: 01.000-0.575-0.170-0.095-0.0340.021-0.1190.067-0.630-0.0010.044-0.0200.204
id-0.5751.0000.042-0.035-0.117-0.1010.028-0.1370.909-0.145-0.198-0.1120.150
popularity-0.1700.0421.0000.5070.6900.3130.8450.3670.1540.5250.6830.6480.364
budget-0.095-0.0350.5071.0000.7430.3150.578-0.0040.0990.9790.6990.4990.286
revenue-0.034-0.1170.6900.7431.0000.3270.7880.2450.0080.7660.9850.9290.330
runtime0.021-0.1010.3130.3150.3271.0000.3350.357-0.0360.3430.3370.2710.189
vote_count-0.1190.0280.8450.5780.7880.3351.0000.4480.1750.5790.7620.7460.441
vote_average0.067-0.1370.367-0.0040.2450.3570.4481.000-0.1010.0290.2650.3300.264
release_year-0.6300.9090.1540.0990.008-0.0360.175-0.1011.000-0.033-0.096-0.0070.117
budget_adj-0.001-0.1450.5250.9790.7660.3430.5790.029-0.0331.0000.7510.5290.284
revenue_adj0.044-0.1980.6830.6990.9850.3370.7620.265-0.0960.7511.0000.9250.327
profit-0.020-0.1120.6480.4990.9290.2710.7460.330-0.0070.5290.9251.0000.332
popularity_level0.2040.1500.3640.2860.3300.1890.4410.2640.1170.2840.3270.3321.000

Missing values

2023-01-31T08:17:36.202497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-31T08:17:36.473649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0idimdb_idpopularitybudgetrevenueoriginal_titlecasthomepagedirectortaglinekeywordsoverviewruntimegenresproduction_companiesrelease_datevote_countvote_averagerelease_yearbudget_adjrevenue_adjprofitpopularity_level
00135397tt036961032.985763150000000.01.513529e+09Jurassic WorldChris Pratt|Bryce Dallas Howard|Irrfan Khan|Vincent D'Onofrio|Nick Robinsonhttp://www.jurassicworld.com/Colin TrevorrowThe park is open.monster|dna|tyrannosaurus rex|velociraptor|islandTwenty-two years after the events of Jurassic Park, Isla Nublar now features a fully functioning dinosaur theme park, Jurassic World, as originally envisioned by John Hammond.124Action|Adventure|Science Fiction|ThrillerUniversal Studios|Amblin Entertainment|Legendary Pictures|Fuji Television Network|Dentsu2015-06-0955626.520151.379999e+081.392446e+091.363529e+09High
1176341tt139219028.419936150000000.03.784364e+08Mad Max: Fury RoadTom Hardy|Charlize Theron|Hugh Keays-Byrne|Nicholas Hoult|Josh Helmanhttp://www.madmaxmovie.com/George MillerWhat a Lovely Day.future|chase|post-apocalyptic|dystopia|australiaAn apocalyptic story set in the furthest reaches of our planet, in a stark desert landscape where humanity is broken, and most everyone is crazed fighting for the necessities of life. Within this world exist two rebels on the run who just might be able to restore order. There's Max, a man of action and a man of few words, who seeks peace of mind following the loss of his wife and child in the aftermath of the chaos. And Furiosa, a woman of action and a woman who believes her path to survival may be achieved if she can make it across the desert back to her childhood homeland.120Action|Adventure|Science Fiction|ThrillerVillage Roadshow Pictures|Kennedy Miller Productions2015-05-1361857.120151.379999e+083.481613e+082.284364e+08High
22262500tt290844613.112507110000000.02.952382e+08InsurgentShailene Woodley|Theo James|Kate Winslet|Ansel Elgort|Miles Tellerhttp://www.thedivergentseries.movie/#insurgentRobert SchwentkeOne Choice Can Destroy Youbased on novel|revolution|dystopia|sequel|dystopic futureBeatrice Prior must confront her inner demons and continue her fight against a powerful alliance which threatens to tear her society apart.119Adventure|Science Fiction|ThrillerSummit Entertainment|Mandeville Films|Red Wagon Entertainment|NeoReel2015-03-1824806.320151.012000e+082.716190e+081.852382e+08High
33140607tt248849611.173104200000000.02.068178e+09Star Wars: The Force AwakensHarrison Ford|Mark Hamill|Carrie Fisher|Adam Driver|Daisy Ridleyhttp://www.starwars.com/films/star-wars-episode-viiJ.J. AbramsEvery generation has a story.android|spaceship|jedi|space opera|3dThirty years after defeating the Galactic Empire, Han Solo and his allies face a new threat from the evil Kylo Ren and his army of Stormtroopers.136Action|Adventure|Science Fiction|FantasyLucasfilm|Truenorth Productions|Bad Robot2015-12-1552927.520151.839999e+081.902723e+091.868178e+09High
44168259tt28208529.335014190000000.01.506249e+09Furious 7Vin Diesel|Paul Walker|Jason Statham|Michelle Rodriguez|Dwayne Johnsonhttp://www.furious7.com/James WanVengeance Hits Homecar race|speed|revenge|suspense|carDeckard Shaw seeks revenge against Dominic Toretto and his family for his comatose brother.137Action|Crime|ThrillerUniversal Pictures|Original Film|Media Rights Capital|Dentsu|One Race Films2015-04-0129477.320151.747999e+081.385749e+091.316249e+09High
55281957tt16632029.110700135000000.05.329505e+08The RevenantLeonardo DiCaprio|Tom Hardy|Will Poulter|Domhnall Gleeson|Paul Andersonhttp://www.foxmovies.com/movies/the-revenantAlejandro González Iñárritu(n. One who has returned, as if from the dead.)father-son relationship|rape|based on novel|mountains|winterIn the 1820s, a frontiersman, Hugh Glass, sets out on a path of vengeance against those who left him for dead after a bear mauling.156Western|Drama|Adventure|ThrillerRegency Enterprises|Appian Way|CatchPlay|Anonymous Content|New Regency Pictures2015-12-2539297.220151.241999e+084.903142e+083.979505e+08High
6687101tt13401388.654359155000000.04.406035e+08Terminator GenisysArnold Schwarzenegger|Jason Clarke|Emilia Clarke|Jai Courtney|J.K. Simmonshttp://www.terminatormovie.com/Alan TaylorReset the futuresaving the world|artificial intelligence|cyborg|killer robot|futureThe year is 2029. John Connor, leader of the resistance continues the war against the machines. At the Los Angeles offensive, John's fears of the unknown future begin to emerge when TECOM spies reveal a new plot by SkyNet that will attack him from both fronts; past and future, and will ultimately change warfare forever.125Science Fiction|Action|Thriller|AdventureParamount Pictures|Skydance Productions2015-06-2325985.820151.425999e+084.053551e+082.856035e+08High
77286217tt36593887.667400108000000.05.953803e+08The MartianMatt Damon|Jessica Chastain|Kristen Wiig|Jeff Daniels|Michael Peñahttp://www.foxmovies.com/movies/the-martianRidley ScottBring Him Homebased on novel|mars|nasa|isolation|botanistDuring a manned mission to Mars, Astronaut Mark Watney is presumed dead after a fierce storm and left behind by his crew. But Watney has survived and finds himself stranded and alone on the hostile planet. With only meager supplies, he must draw upon his ingenuity, wit and spirit to subsist and find a way to signal to Earth that he is alive.141Drama|Adventure|Science FictionTwentieth Century Fox Film Corporation|Scott Free Productions|Mid Atlantic Films|International Traders|TSG Entertainment2015-09-3045727.620159.935996e+075.477497e+084.873803e+08High
88211672tt22936407.40416574000000.01.156731e+09MinionsSandra Bullock|Jon Hamm|Michael Keaton|Allison Janney|Steve Cooganhttp://www.minionsmovie.com/Kyle Balda|Pierre CoffinBefore Gru, they had a history of bad bossesassistant|aftercreditsstinger|duringcreditsstinger|evil mastermind|minionsMinions Stuart, Kevin and Bob are recruited by Scarlet Overkill, a super-villain who, alongside her inventor husband Herb, hatches a plot to take over the world.91Family|Animation|Adventure|ComedyUniversal Pictures|Illumination Entertainment2015-06-1728936.520156.807997e+071.064192e+091.082731e+09High
99150540tt20966736.326804175000000.08.537086e+08Inside OutAmy Poehler|Phyllis Smith|Richard Kind|Bill Hader|Lewis Blackhttp://movies.disney.com/inside-outPete DocterMeet the little voices inside your head.dream|cartoon|imaginary friend|animation|kidGrowing up can be a bumpy road, and it's no exception for Riley, who is uprooted from her Midwest life when her father starts a new job in San Francisco. Like all of us, Riley is guided by her emotions - Joy, Fear, Anger, Disgust and Sadness. The emotions live in Headquarters, the control center inside Riley's mind, where they help advise her through everyday life. As Riley and her emotions struggle to adjust to a new life in San Francisco, turmoil ensues in Headquarters. Although Joy, Riley's main and most important emotion, tries to keep things positive, the emotions conflict on how best to navigate a new city, house and school.94Comedy|Animation|FamilyWalt Disney Pictures|Pixar Animation Studios|Walt Disney Studios Motion Pictures2015-06-0939358.020151.609999e+087.854116e+086.787086e+08High
Unnamed: 0idimdb_idpopularitybudgetrevenueoriginal_titlecasthomepagedirectortaglinekeywordsoverviewruntimegenresproduction_companiesrelease_datevote_countvote_averagerelease_yearbudget_adjrevenue_adjprofitpopularity_level
1277103388291tt01078400.31379214000000.027515786.0Poetic JusticeJanet Jackson|Tupac Shakur|Regina King|Joe Torry|Tyra Ferrellhttp://www.janetjackson.comJohn SingletonA Street Romance.loss of lover|sadness|los angeles|road movieIn this film, we see the world through the eyes of main character Justice, a young African-American poet. A mail carrier invites a few friends along for a long overnight delivery run.109Drama|RomanceColumbia Pictures1993-07-23246.819932.113258e+074.153425e+0713515786.0Low
127810401667tt00625121.5548089500000.0111584787.0You Only Live TwiceSean Connery|Akiko Wakabayashi|Karin Dor|Mie Hama|TetsurÅ Tambahttp://www.mgm.com/view/movie/2347/You-Only-Live-Twice/Lewis GilbertYou Only Live Twice...and Twice is the only way to live!london|japan|england|assassination|helicopterA mysterious space craft kidnaps a Russian and American space capsule and brings the world on the verge of another World War. James Bond investigates the case in Japan and meets with his archenemy Blofeld. The fifth film from the legendary James Bond series starring Sean Connery as the British super agent.117Action|Thriller|AdventureEon Productions2067-06-123016.219676.209926e+077.294034e+08102084787.0Moderately High
127910438657tt00570762.5082352500000.078898765.0From Russia With LoveSean Connery|Daniela Bianchi|Lotte Lenya|Robert Shaw|Bernard Leehttp://www.mgm.com/view/movie/717/From-Russia-With-Love/Terence YoungThe world's masters of murder pull out all the stops to destroy Agent 007!venice|london|terror|england|assassinationAgent 007 is back in the second installment of the James Bond series, this time battling a secret crime organization known as SPECTRE. Russians Rosa Klebb and Kronsteen are out to snatch a decoding device known as the Lektor, using the ravishing Tatiana to lure Bond into helping them. Bond willingly travels to meet Tatiana in Istanbul, where he must rely on his wits to escape with his life in a series of deadly encounters with the enemy115Action|Thriller|AdventureEon Productions|Metro-Goldwyn-Mayer (MGM)|Danjaq2063-10-114586.719631.780045e+075.617734e+0876398765.0High
1280104896978tt00907280.96098425000000.011000000.0Big Trouble in Little ChinaKurt Russell|Kim Cattrall|Dennis Dun|James Hong|Victor Wonghttp://www.theofficialjohncarpenter.com/big-trouble-in-little-china/John CarpenterAdventure doesn't come any bigger!kung fu|chinatown|magicWhen trucker Jack Burton agreed to take his friend Wang Chi to pick up his fiancee at the airport, he never expected to get involved in a supernatural battle between good and evil. Wang's fiancee has emerald green eyes, which make her a perfect target for an immortal sorcerer named Lo Pan and his three invincible cronies. Lo Pan must marry a girl with green eyes so he can regain his physical form.99Adventure|Fantasy|Action|ComedyTwentieth Century Fox Film Corporation|TAFT Entertainment Pictures1986-05-303476.719864.973516e+072.188347e+07-14000000.0Medium
1281105949552tt00700472.0107338000000.0441306145.0The ExorcistLinda Blair|Max von Sydow|Ellen Burstyn|Jason Miller|Lee J. Cobbhttp://theexorcist.warnerbros.com/William FriedkinSomething almost beyond comprehension is happening to a girl on this street, in this house... and a man has been sent for as a last resort. This man is The Exorcist.exorcism|holy water|religion and supernatural|vomit|christian12-year-old Regan MacNeil begins to adapt an explicit new personality as strange events befall the local area of Georgetown. Her mother becomes torn between science and superstition in a desperate bid to save her daughter, and ultimately turns to her last hope: Father Damien Karras, a troubled priest who is struggling with his own faith.122Drama|Horror|ThrillerWarner Bros.|Hoya Productions1973-12-2611137.219733.928928e+072.167325e+09433306145.0Moderately High
128210595253tt00703281.5491397000000.0161777836.0Live and Let DieRoger Moore|Yaphet Kotto|Jane Seymour|Clifton James|Julius Harrishttp://www.mgm.com/view/movie/1130/Live-and-Let-Die/Guy HamiltonRoger Moore is James Bond.london|new york|bomb|england|spyJames Bond must investigate a mysterious murder case of a British agent in New Orleans. Soon he finds himself up against a gangster boss named Mr. Big.121Adventure|Action|ThrillerEon Productions|Metro-Goldwyn-Mayer (MGM)1973-07-052936.119733.437812e+077.945168e+08154777836.0Moderately High
128310689660tt00598001.91046511000000.0141195658.0ThunderballSean Connery|Claudine Auger|Adolfo Celi|Luciana Paluzzi|Rik Van Nutterhttp://www.mgm.com/view/movie/2009/Thunderball/Terence YoungLook up! Look down! Look out!paris|florida|fighter pilot|sanatorium|secret organizationA criminal organization has obtained two nuclear bombs and are asking for a 100 million pound ransom in the form of diamonds in seven days or they will use the weapons. The secret service sends James Bond to the Bahamas to once again save the world.130Adventure|Action|ThrillerEon Productions|Metro-Goldwyn-Mayer (MGM)2065-12-163316.319657.612620e+079.771535e+08130195658.0Moderately High
128410724668tt00647571.7787467000000.081974493.0On Her Majesty's Secret ServiceGeorge Lazenby|Diana Rigg|Telly Savalas|Gabriele Ferzetti|Ilse Steppathttp://www.mgm.com/view/movie/1411/On-Her-Majesty%E2%80%99s-Secret-Service/Peter R. HuntFar up! Far out! Far more! James Bond 007 is back!london|suicide|england|switzerland|secret identityJames Bond tracks archnemesis Ernst Blofeld to a mountaintop retreat where he's training an army of beautiful but lethal women. Along the way, Bond falls for Italian contessa Tracy Draco -- and marries her in order to get closer to Blofeld. Meanwhile, he locates Blofeld in the Alps and embarks on a classic ski chase.142Adventure|Action|ThrillerEon Productions|Metro-Goldwyn-Mayer (MGM)|Danjaq2069-12-122586.419694.160985e+074.872780e+0874974493.0Moderately High
128510759948tt00776511.198849300000.070000000.0HalloweenDonald Pleasence|Jamie Lee Curtis|P.J. Soles|Nancy Kyes|Nick Castlehttp://www.theofficialjohncarpenter.com/halloween/John CarpenterThe Night HE Came Home!female nudity|nudity|mask|babysitter|halloweenA psychotic murderer, institutionalized since childhood for the murder of his sister, escapes and stalks a bookish teenage girl and her friends while his doctor chases him through the streets.91Horror|ThrillerCompass International Pictures|Falcon International Productions1978-10-255227.319781.002810e+062.339890e+0869700000.0Moderately High
1286107608469tt00779751.1579302700000.0141000000.0Animal HouseJohn Belushi|Tim Matheson|John Vernon|Verna Bloom|Tom Hulcehttp://www.animalhouse.com/John LandisIt was the Deltas against the rules... the rules lost!female nudity|sex|nudity|collage|fraternityAt a 1962 College, Dean Vernon Wormer is determined to expel the entire Delta Tau Chi Fraternity, but those troublemakers have other plans for him.109ComedyUniversal Pictures|Oregon Film Factory|Stage III Productions1978-07-272306.719789.025292e+064.713208e+08138300000.0Moderately High